Reconstruction of a Context-Specific Model Based on Genome-Scale Metabolic Simulation for Identification of Prochloraz Resistance Mechanisms in Penicillium digitatum

ABSTRACTWith the publication of high-quality genome-scale metabolic models for several organisms, the Systems Biology community has developed a number of algorithms for their analysis making use of ever growing –omics data. In particular, the reconstruction of the first genome-scale human metabolic model, Recon1, promoted the development of Context-Specific Model (CS-Model) reconstruction methods. This family of algorithms aims to identify the set of metabolic reactions that are active in a cell in a given condition using omics data, such as gene expression levels. Different CS-Model reconstruction algorithms have their own strengths and weaknesses depending on the problem under study and omics data available. However, after careful inspection, we found that all of these algorithms share common issues in the way GPR rules and gene expression data are treated. The first issue is related with how gapfilling reactions are managed after the reconstruction is conducted. The second issue concerns the molecular context, which is used to build the CS-model but neglected for posterior analyses. To evaluate the effect of these issues, we reconstructed ∼400 CS-Models of cancer cell lines and conducted gene essentiality analysis, using CRISPR–Cas9 essentiality data for validation purposes. Altogether, our results illustrate the importance of correcting the errors introduced during the GPR translation in many of the published metabolic reconstructions.

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XomicsToModel: Multiomics data integration and generation of thermodynamically consistent metabolic models

10.1101/2021.11.08.467803 ◽

2021 ◽

Author(s):

German Preciat ◽

Agnieszka B. Wegrzyn ◽

Ines Thiele ◽

Thomas Hankemeier ◽

Ronan MT Fleming

Keyword(s):

Data Integration ◽

Input Data ◽

Metabolic Model ◽

Specific Model ◽

Biochemical Network ◽

Metabolic Reconstruction ◽

Generic Model ◽

Metabolomic Data ◽

Context Specific ◽

Genome Scale

Constraint-based modelling can mechanistically simulate the behaviour of a biochemical system, permitting hypotheses generation, experimental design and interpretation of experimental data, with numerous applications, including modelling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in a given context. However, existing model extraction algorithms are unable to ensure that the context-specific model is thermodynamically feasible. This protocol introduces XomicsToModel, a semi-automated pipeline that integrates bibliomic, transcriptomic, proteomic, and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. The XomicsToModel pipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model, but it enables multi-omic data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. With all input data fully prepared, algorithmic completion of the pipeline takes ~10 min, however manual review of intermediate results may also be required, e.g., when inconsistent input data lead to an infeasible model.

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Functional Genomics Approaches to Elucidate Vulnerabilities of Intrinsic and Acquired Chemotherapy Resistance

Cells ◽

10.3390/cells10020260 ◽

2021 ◽

Vol 10 (2) ◽

pp. 260

Author(s):

Ronay Cetin ◽

Eva Quandt ◽

Manuel Kaulich

Keyword(s):

Drug Resistance ◽

Acquired Resistance ◽

Chemotherapy Resistance ◽

Resistance Mechanisms ◽

Therapy Failure ◽

Tightly Coupled ◽

Unbiased Gene ◽

Multiple Drugs ◽

Genome Scale ◽

Gene Perturbations

Drug resistance is a commonly unavoidable consequence of cancer treatment that results in therapy failure and disease relapse. Intrinsic (pre-existing) or acquired resistance mechanisms can be drug-specific or be applicable to multiple drugs, resulting in multidrug resistance. The presence of drug resistance is, however, tightly coupled to changes in cellular homeostasis, which can lead to resistance-coupled vulnerabilities. Unbiased gene perturbations through RNAi and CRISPR technologies are invaluable tools to establish genotype-to-phenotype relationships at the genome scale. Moreover, their application to cancer cell lines can uncover new vulnerabilities that are associated with resistance mechanisms. Here, we discuss targeted and unbiased RNAi and CRISPR efforts in the discovery of drug resistance mechanisms by focusing on first-in-line chemotherapy and their enforced vulnerabilities, and we present a view forward on which measures should be taken to accelerate their clinical translation.

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Deep Brain Stimulation of the Pallidum Internum for Gilles de la Tourette Syndrome: A Patient-Specific Model-Based Simulation Study of the Electric Field

Neuromodulation Technology at the Neural Interface ◽

10.1111/ner.12248 ◽

2014 ◽

Vol 18 (2) ◽

pp. 90-96 ◽

Cited By ~ 13

Author(s):

Karin Wårdell ◽

Zinovia Kefalopoulou ◽

Elin Diczfalusy ◽

Mats Andersson ◽

Mattias Åström ◽

...

Keyword(s):

Deep Brain Stimulation ◽

Electric Field ◽

Tourette Syndrome ◽

Brain Stimulation ◽

Simulation Study ◽

Specific Model ◽

Patient Specific ◽

Model Based ◽

Deep Brain ◽

Stimulation Of

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Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-018-1703-0 ◽

2018 ◽

Vol 13 (3) ◽

pp. 331-342 ◽

Cited By ~ 4

Author(s):

Elisee Ilunga-Mbuyamba ◽

Juan Gabriel Avina-Cervantes ◽

Dirk Lindner ◽

Felix Arlt ◽

Jean Fulbert Ituna-Yudonago ◽

...

Keyword(s):

Brain Tumors ◽

Intraoperative Ultrasound ◽

Specific Model ◽

Patient Specific ◽

Ultrasound Images ◽

Model Based

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Identifying functional metabolic shifts in heart failure with the integration of omics data and a cardiomyocyte-specific, genome-scale model

10.1101/2020.07.20.212274 ◽

2020 ◽

Author(s):

Bonnie V. Dougherty ◽

Kristopher D. Rawls ◽

Glynis L. Kolling ◽

Kalyan C. Vinnakota ◽

Anders Wallqvist ◽

...

Keyword(s):

Gene Expression ◽

Heart Failure ◽

Specific Model ◽

Metabolic Phenotype ◽

Scale Model ◽

Metabolic Markers ◽

Metabolic Functions ◽

Carbon Substrates ◽

End Stage ◽

Genome Scale

SummaryThe heart is a metabolic omnivore, known to consume many different carbon substrates in order to maintain function. In diseased states, the heart’s metabolism can shift between different carbon substrates; however, there is some disagreement in the field as to the metabolic shifts seen in end-stage heart failure and whether all heart failure converges to a common metabolic phenotype. Here, we present a new, validated cardiomyocyte-specific GEnome-scale metabolic Network REconstruction (GENRE), iCardio, and use the model to identify common shifts in metabolic functions across heart failure omics datasets. We demonstrate the utility of iCardio in interpreting heart failure gene expression data by identifying Tasks Inferred from Differential Expression (TIDEs) which represent metabolic functions associated with changes in gene expression. We identify decreased NO and Neu5Ac synthesis as common metabolic markers of heart failure across datasets. Further, we highlight the differences in metabolic functions seen across studies, further highlighting the complexity of heart failure. The methods presented for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissue and diseases of interest.

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Improved Discovery of Molecular Interactions in Genome-Scale Data with Adaptive Model-Based Normalization

PLoS ONE ◽

10.1371/journal.pone.0053930 ◽

2013 ◽

Vol 8 (1) ◽

pp. e53930 ◽

Cited By ~ 2

Author(s):

Julia Salzman ◽

Daniel M. Klass ◽

Patrick O. Brown

Keyword(s):

Molecular Interactions ◽

Adaptive Model ◽

Model Based ◽

Genome Scale ◽

Scale Data

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Fungicidal Actions and Resistance Mechanisms of Prochloraz to Penicillium digitatum

Plant Disease ◽

10.1094/pdis-05-20-1128-re ◽

2020 ◽

pp. PDIS-05-20-1128

Author(s):

Yuchao Zhang ◽

Bao Zhang ◽

Chaoxi Luo ◽

Yanping Fu ◽

Fuxing Zhu

Keyword(s):

Cell Membrane ◽

Target Genes ◽

Membrane Integrity ◽

Point Mutations ◽

Resistance Mechanisms ◽

Penicillium Digitatum ◽

Cell Membrane Permeability ◽

Baseline Sensitivity ◽

Cell Membrane Integrity ◽

Citrus Groves

The demethylation inhibitor (DMI) fungicide prochloraz has been widely used in China to control citrus green mold, which is caused by Penicillium digitatum. The 50% effective concentration (EC50) values of prochloraz for 129 isolates of P. digitatum collected in 2017 from citrus groves of four provinces of China ranged from 0.0032 to 0.4582 mg/liter. Analysis of the distribution of natural logarithms of EC50 values indicated that 111 isolates with EC50 values lower than 0.05 mg/liter could be considered sensitive to prochloraz. Relative baseline sensitivity was established based on the 111 sensitive isolates, and the mean EC50 value was 0.0090 ± 0.0054 mg/liter (SD). Prochloraz at 60, 100, and 140 mg/liter provided preventive efficacies of 67.8, 93.0, and 96.4%, respectively. Prochloraz at 0.005 and 0.01 mg/liter disrupted cell membrane integrity of conidia but reduced cell membrane permeability of mycelia. Prochloraz at 0.01 mg/liter reduced ergosterol content in mycelia by 41.8%. Two prochloraz-resistant isolates with EC50 values of 3.97 and 5.68 mg/liter were attained by consecutive subculturing on prochloraz-amended PDA. Studies on the expression levels of three potential target genes, CYP51A, CYP51B, and CYP51C, demonstrated that whether in the absence or presence of prochloraz, only CYP51B in the resistant isolates was overexpressed at least 10-fold higher than that of the sensitive ones. Sequencing of the three genes showed that only CYP51B in the resistant isolates had a 199-bp insertion in the promoter region. In addition, only CYP51B displayed point mutations of G405S, G389C, and Y390S in the coding regions in the resistant isolates. These results were important for understanding the resistance mechanisms of P. digitatum to prochloraz.

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Pyrimethanil Sensitivity and Resistance Mechanisms in Penicillium digitatum

Plant Disease ◽

10.1094/pdis-08-20-1739-re ◽

2020 ◽

Author(s):

Yuchao Zhang ◽

Yanping Fu ◽

Chaoxi Luo ◽

Fuxing Zhu

Keyword(s):

Target Genes ◽

Sodium Dodecyl ◽

Resistance Mechanisms ◽

Penicillium Digitatum ◽

Cross Resistance ◽

Postharvest Diseases ◽

Cell Wall Degrading Enzymes ◽

Baseline Sensitivity ◽

Nucleotide Mutation ◽

Resistant Mutants

Pyrimethanil is an anilinopyrimidines (AP) fungicide and highly effective in controlling green mold caused by Penicillium digitatum but has not yet been registered in China to control postharvest diseases of citrus. In the present study, baseline sensitivity of P. digitatum to pyrimethanil was established based on the effective concentrations for 50% inhibition (EC50) values of 127 isolates collected from five major citrus-growing regions of China. The distribution of these EC50 values was unimodal but with a long right tail. The mean EC50 value was 0.137 ± 0.046 μg/mL (SD), and the minimum and maximum were 0.073 and 0.436 μg/mL, respectively. Pyrimethanil in potato dextrose agar (PDA) at 0.20 μg/mL decreased methionine production in the mycelia by 21.6% and reduced the activities of cell wall-degrading enzymes cellulase and pectinase by 9.1 and 32.8%, respectively. Twelve pyrimethanil-resistant mutants were obtained by consecutive sub-culturing of 12 arbitrarily selected sensitive isolates on pyrimethanil-amended PDA for 4 generations, and the resistance factors ranged from 69 to 3421. There was no cross-resistance between pyrimethanil and prochloraz (r = 0.377, P = 0.123). Compared with their parental isolates, pyrimethanil-resistant mutants had reduced pathogenicity to citrus fruit but higher tolerance to hydrogen peroxide. No differences were detected in tolerance to NaCl, CaCl2, Congo red, or sodium dodecyl sulfate (SDS). Exogenous addition of methionine into PDA partially alleviated the toxicity of pyrimethanil to the sensitive isolates but had no significant effect on toxicity to the resistant mutants. Sequencing of cystathionine γ-synthase encoding genes CGS1 and CGS2, the potential target genes for pyrimethanil, showed that there was no nucleotide mutation in the coding region of CGS of the pyrimethanil-resistant mutants. However, the relative expression of CGS1 and CGS2 genes of the pyrimethanil-resistant mutants was reduced by 42.5 and 57.4%, respectively. These results have important implications for applications of pyrimethanil to control P. digitatum and for understanding the modes of action and resistance mechanisms of pyrimethanil.

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